

import argparse
import re
from typing import Dict

import torch
from datasets import Audio, Dataset, load_dataset, load_metric

from transformers import AutoFeatureExtractor, pipeline






def        snake_case__		(    lowerCamelCase_     ,				lowerCamelCase_		):
							A      :					List[str]      		=					args.log_outputs
							A      :					Optional[Any]      		=					'''_'''.join(args.dataset.split('''/'''		) + [args.config, args.split]		)

							# load metric
							A      :					List[str]      		=					load_metric('''wer'''		)
							A      :					Optional[int]      		=					load_metric('''cer'''		)

							# compute metrics
							A      :					Any      		=					wer.compute(references=result['''target''']     ,				predictions=result['''prediction''']		)
							A      :					Dict      		=					cer.compute(references=result['''target''']     ,				predictions=result['''prediction''']		)

							# print & log results
							A      :					Tuple      		=					F'WER: {wer_result}\nCER: {cer_result}'
							print(lowerCamelCase_		)

							with open(F'{dataset_id}_eval_results.txt'     ,				'''w'''		) as f:
														f.write(lowerCamelCase_		)

							# log all results in text file. Possibly interesting for analysis
							if log_outputs is not None:
														A      :					int      		=					F'log_{dataset_id}_predictions.txt'
														A      :					Dict      		=					F'log_{dataset_id}_targets.txt'

														with open(lowerCamelCase_     ,				'''w'''		) as p, open(lowerCamelCase_     ,				'''w'''		) as t:
																					# mapping function to write output
																					def write_to_file(lowerCamelCase_     ,				lowerCamelCase_		):
																												p.write(F'{i}' + '''\n'''		)
																												p.write(batch['''prediction'''] + '''\n'''		)
																												t.write(F'{i}' + '''\n'''		)
																												t.write(batch['''target'''] + '''\n'''		)

																					result.map(lowerCamelCase_     ,				with_indices=lowerCamelCase_		)






def        snake_case__		(    lowerCamelCase_		):
							A      :					Optional[int]      		=					'''[,?.!\-\;\:"“%‘”�—’…–]'''  # noqa: W605 IMPORTANT: this should correspond to the chars that were ignored during training

							A      :					Any      		=					re.sub(lowerCamelCase_     ,				''''''     ,				text.lower()		)

							# In addition, we can normalize the target text, e.g. removing new lines characters etc...
							# note that order is important here!
							A      :					Any      		=					['''\n\n''', '''\n''', '''   ''', '''  ''']

							for t in token_sequences_to_ignore:
														A      :					Dict      		=					''' '''.join(text.split(lowerCamelCase_		)		)

							return text






def        snake_case__		(    lowerCamelCase_		):
							# load dataset
							A      :					str      		=					load_dataset(args.dataset     ,				args.config     ,				split=args.split     ,				use_auth_token=lowerCamelCase_		)

							# for testing: only process the first two examples as a test
							# dataset = dataset.select(range(10))

							# load processor
							A      :					int      		=					AutoFeatureExtractor.from_pretrained(args.model_id		)
							A      :					int      		=					feature_extractor.sampling_rate

							# resample audio
							A      :					Union[str, Any]      		=					dataset.cast_column('''audio'''     ,				Audio(sampling_rate=lowerCamelCase_		)		)

							# load eval pipeline
							if args.device is None:
														A      :					Any      		=					0 if torch.cuda.is_available() else -1
							A      :					Dict      		=					pipeline('''automatic-speech-recognition'''     ,				model=args.model_id     ,				device=args.device		)

							# map function to decode audio
							def map_to_pred(lowerCamelCase_		):
														A      :					Dict      		=					asr(
														    batch['''audio''']['''array''']     ,				chunk_length_s=args.chunk_length_s     ,				stride_length_s=args.stride_length_s		)

														A      :					List[Any]      		=					prediction['''text''']
														A      :					Union[str, Any]      		=					normalize_text(batch['''sentence''']		)
														return batch

							# run inference on all examples
							A      :					Any      		=					dataset.map(lowerCamelCase_     ,				remove_columns=dataset.column_names		)

							# compute and log_results
							# do not change function below
							log_results(lowerCamelCase_     ,				lowerCamelCase_		)


if __name__ == "__main__":
		lowercase			:				Any   =     argparse.ArgumentParser()

		parser.add_argument(
		    "--model_id", type=str, required=True, help="Model identifier. Should be loadable with 🤗 Transformers"
		)
		parser.add_argument(
		    "--dataset",
		    type=str,
		    required=True,
		    help="Dataset name to evaluate the `model_id`. Should be loadable with 🤗 Datasets",
		)
		parser.add_argument(
		    "--config", type=str, required=True, help="Config of the dataset. *E.g.* `'en'`  for Common Voice"
		)
		parser.add_argument("--split", type=str, required=True, help="Split of the dataset. *E.g.* `'test'`")
		parser.add_argument(
		    "--chunk_length_s", type=float, default=None, help="Chunk length in seconds. Defaults to 5 seconds."
		)
		parser.add_argument(
		    "--stride_length_s", type=float, default=None, help="Stride of the audio chunks. Defaults to 1 second."
		)
		parser.add_argument(
		    "--log_outputs", action="store_true", help="If defined, write outputs to log file for analysis."
		)
		parser.add_argument(
		    "--device",
		    type=int,
		    default=None,
		    help="The device to run the pipeline on. -1 for CPU (default), 0 for the first GPU and so on.",
		)
		lowercase			:				Union[str, Any]   =     parser.parse_args()

		main(args)









import json
from typing import List, Optional, Tuple

from tokenizers import normalizers

from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_convbert import ConvBertTokenizer


lowercase			:				Optional[int]   =     logging.get_logger(__name__)

lowercase			:				Optional[Any]   =     {"vocab_file": "vocab.txt"}

lowercase			:				Optional[int]   =     {
    "vocab_file": {
        "YituTech/conv-bert-base": "https://huggingface.co/YituTech/conv-bert-base/resolve/main/vocab.txt",
        "YituTech/conv-bert-medium-small": (
            "https://huggingface.co/YituTech/conv-bert-medium-small/resolve/main/vocab.txt"
        ),
        "YituTech/conv-bert-small": "https://huggingface.co/YituTech/conv-bert-small/resolve/main/vocab.txt",
    }
}

lowercase			:				Tuple   =     {
    "YituTech/conv-bert-base": 5_12,
    "YituTech/conv-bert-medium-small": 5_12,
    "YituTech/conv-bert-small": 5_12,
}


lowercase			:				Optional[int]   =     {
    "YituTech/conv-bert-base": {"do_lower_case": True},
    "YituTech/conv-bert-medium-small": {"do_lower_case": True},
    "YituTech/conv-bert-small": {"do_lower_case": True},
}







class 					__lowercase  (						_SCREAMING_SNAKE_CASE				):





							"""simple docstring"""

							UpperCAmelCase_						:		int								=	VOCAB_FILES_NAMES
							UpperCAmelCase_						:		Optional[int]								=	PRETRAINED_VOCAB_FILES_MAP
							UpperCAmelCase_						:		List[Any]								=	PRETRAINED_INIT_CONFIGURATION
							UpperCAmelCase_						:		Optional[Any]								=	PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
							UpperCAmelCase_						:		Optional[int]								=	ConvBertTokenizer




							def __init__(					self    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=None    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase="[UNK]"    ,						__UpperCAmelCase="[SEP]"    ,						__UpperCAmelCase="[PAD]"    ,						__UpperCAmelCase="[CLS]"    ,						__UpperCAmelCase="[MASK]"    ,						__UpperCAmelCase=True    ,						__UpperCAmelCase=None    ,						**__UpperCAmelCase    ,						)		->       Optional[int]:
														super().__init__(
														    __UpperCAmelCase    ,						tokenizer_file=__UpperCAmelCase    ,						do_lower_case=__UpperCAmelCase    ,						unk_token=__UpperCAmelCase    ,						sep_token=__UpperCAmelCase    ,						pad_token=__UpperCAmelCase    ,						cls_token=__UpperCAmelCase    ,						mask_token=__UpperCAmelCase    ,						tokenize_chinese_chars=__UpperCAmelCase    ,						strip_accents=__UpperCAmelCase    ,						**__UpperCAmelCase    ,						)

														A      :					Any      		=					json.loads(self.backend_tokenizer.normalizer.__getstate__()						)
														if (
														    normalizer_state.get('''lowercase'''    ,						__UpperCAmelCase						) != do_lower_case
														    or normalizer_state.get('''strip_accents'''    ,						__UpperCAmelCase						) != strip_accents
														    or normalizer_state.get('''handle_chinese_chars'''    ,						__UpperCAmelCase						) != tokenize_chinese_chars
														):
																					A      :					Tuple      		=					getattr(__UpperCAmelCase    ,						normalizer_state.pop('''type'''						)						)
																					A      :					int      		=					do_lower_case
																					A      :					int      		=					strip_accents
																					A      :					Union[str, Any]      		=					tokenize_chinese_chars
																					A      :					str      		=					normalizer_class(**__UpperCAmelCase						)

														A      :					str      		=					do_lower_case




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase=None						)		->       Optional[int]:
														A      :					Dict      		=					[self.cls_token_id] + token_ids_a + [self.sep_token_id]

														if token_ids_a:
																					output += token_ids_a + [self.sep_token_id]

														return output




							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       List[int]:
														A      :					List[Any]      		=					[self.sep_token_id]
														A      :					Optional[Any]      		=					[self.cls_token_id]
														if token_ids_a is None:
																					return len(cls + token_ids_a + sep						) * [0]
														return len(cls + token_ids_a + sep						) * [0] + len(token_ids_a + sep						) * [1]






							def       snake_case			(					self    ,						__UpperCAmelCase    ,						__UpperCAmelCase = None						)		->       Tuple[str]:
														A      :					Optional[int]      		=					self._tokenizer.model.save(__UpperCAmelCase    ,						name=__UpperCAmelCase						)
														return tuple(__UpperCAmelCase						)






